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<div style="display: none; max-height: 0px; overflow: hidden;">The core essence and challenges of data engineering remain unchanged despite decades of tool evolution from early ETL/SSIS in the 2000s </div>
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<h1><strong>TLDR Data <span id="date">2026-01-15</span></strong></h1>
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<h1><strong>Deep Dives</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.uber.com%2Fblog%2Ffrom-static-rate-limiting-to-intelligent-load-management%2F%3Futm_source=tldrdata/1/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/uK1Ca_V3-uC9NpEBMikTjHwCBMqq7lDZZjYlY1moXGI=440">
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<strong>How Uber Conquered Database Overload: The Journey from Static Rate-Limiting to Intelligent Load Management (12 minute read)</strong>
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Uber replaced static rate limiting with Cinnamon, an intelligent load management system embedded in its storage layer. It used concurrency-based detection, priority queues (t0–t5), pluggable regulators, and PID auto-tuning to dynamically shed load and adjust timeouts. This achieved 80% higher throughput, ~70% lower P99 latency, and ~93% fewer peak goroutines.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Feng.lyft.com%2Flyfts-feature-store-architecture-optimization-and-evolution-7835f8962b99%3Fgi=19d0125dd862%26utm_source=tldrdata/1/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/ivpbM43bl6cseHfDCT2ER8SybV3HJ2LHpF-_ZCf2jl0=440">
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<strong>Lyft's Feature Store: Architecture, Optimization, and Evolution (11 minute read)</strong>
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Lyft's Feature Store computes batch features daily via Spark SQL/Hive, serves online features through DynamoDB primary store + Valkey caching + OpenSearch for embeddings, and streams features processed in real-time with Apache Flink from Kafka/Kinesis. Evolutions include migration to Astronomer for orchestration, data contracts for quality/freshness, and improved discoverability via Amundsen.
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<strong>Zeta's Lakehouse Journey: A Composable, Scalable, and Federated Architecture (5 minute read)</strong>
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Zeta built a composable, scalable, and federated Lakehouse architecture with Apache Iceberg on S3Table/AWS Glue Catalog and a multi-account federated setup to unify heterogeneous data from acquisitions, enable secure cross-team access without duplication, support diverse compute engines (Spark, Snowflake, ClickHouse, and Trino) for AI/marketing workloads, and ensure governance, schema evolution, and vendor independence.
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<strong>Building a Global, Event-Driven Platform: Our Ongoing Journey, Part 2 (5 minute read)</strong>
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Vinted built a global, event-driven platform by centralizing all writes in a single primary region to maintain a consistent source of truth, replicating read-optimized projections to edge regions for fast local access, embracing eventual consistency with idempotent and out-of-order-tolerant event handling, shifting teams to own domain-specific asynchronous projections (such as for feeds and search), and avoiding multi-region sharding complexity.
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<h1><strong>Opinions & Advice</strong></h1>
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<strong>A Diary of a Data Engineer (14 minute read)</strong>
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The core essence and challenges of data engineering remain unchanged despite decades of tool evolution from early ETL/SSIS in the 2000s, through Hadoop/big data hype, to modern stacks like dbt, Iceberg, and AI agents in 2026. Key insights include the eternal loop ("The tools change. The loop doesn't."), the lost art of proper data modeling, treating schema issues as people problems, and focusing on fundamentals over trends.
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<strong>Why 90% Accuracy in Text-to-SQL is 100% Useless (9 minute read)</strong>
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Text-to-SQL solutions powered by LLMs hold immense potential for self-service analytics, but enterprise adoption hinges on 100% accuracy, as any hallucination or error erodes trust and adoption. Even leading integrated platforms such as BigQuery critically lack evaluation capability and fail to solve the accuracy problem, creating real risk, as inefficient SQL can trigger extreme runaway query costs. Outdated benchmarking misrepresents real-world readiness.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhackernoon.com%2Fzero-trust-data-access-for-ai-training-new-architecture-patterns-for-cloud-and-on-prem-workloads%3Futm_source=tldrdata/1/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/AwoQPGjW99aBGMe2Sg_ab0uEfyz9V0MN_Q35M9_5mvU=440">
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<strong>Zero-Trust Data Access for AI Training: New Architecture Patterns for Cloud and On-Prem Workloads (6 minute read)</strong>
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Zero-trust data access models are crucial for securing sensitive, fragmented data powering modern AI workloads across hybrid, cloud, and edge environments. By using workload identities, metadata-driven policies, and continuous verification, zero-trust enables fine-grained, context-aware control over data access (even during processing). Organizations deploying zero-trust reported $1.76 million lower average breach costs.
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<h1><strong>Launches & Tools</strong></h1>
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<strong>Connect your first data source in <10 minutes with 700+ prebuilt connectors from Fivetran (Sponsor)</strong>
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Working with an ever-growing set of data sources that don't talk to one another? Spend your time on something other than wrangling APIs. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgo.fivetran.com%2Fsignups%2Fsmb%3Futm_source=TLDR%2520data%26utm_medium=sponsored-ad%26utm_campaign=SC-PLG-brand%2520Q4%26utm_content=46037/2/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/3Lqb4KSAT32LgxEKsxFuWrlZd2XfEhk6_lXFftj2LY8=440" rel="noopener noreferrer nofollow" target="_blank"><span>Fivetran's</span></a> free trial includes over 700 connectors, allowing you to replace manual integrations with zero-maintenance data automation. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgo.fivetran.com%2Fsignups%2Fsmb%3Futm_source=TLDR%2520data%26utm_medium=sponsored-ad%26utm_campaign=SC-PLG-brand%2520Q4%26utm_content=46037/3/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/y1BlWRTVUAgnTBeeDcZ1edQBBDSwI6dEkPH6w9HiXvY=440" rel="noopener noreferrer nofollow" target="_blank"><span>Try it today</span></a> — <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgo.fivetran.com%2Fsignups%2Fsmb%3Futm_source=TLDR%2520data%26utm_medium=sponsored-ad%26utm_campaign=SC-PLG-brand%2520Q4%26utm_content=46037/4/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/xqU12AnS4QIa4HFf-zcLfJSShYqhKbQHppVjNri0wOY=440" rel="noopener noreferrer nofollow" target="_blank"><span>no credit card needed</span></a>.
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AutoRAG is a system that automatically tests and tunes different RAG pipeline setups to find what works best for each task. Instead of hand-tuning chunking, retrieval, reranking, and prompts, you define the evaluation metric and let it optimize the pipeline end-to-end.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>The new observability stack war in 2026 (5 minute read)</strong>
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Observability and infrastructure are converging as infra vendors aggressively move into the observability stack, targeting control over the telemetry data plane and agent-layer budgets. The combination of OpenTelemetry for ingest and Apache Iceberg for an open, portable storage layer is emerging as the new standard, which is motivated by cost control, data portability, and AI-driven parallel investigations that strain proprietary systems. Efficient write paths remain a key challenge. Streaming engines like RisingWave are positioned to address issues like small files and schema drift at scale.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fthenewaiorder.substack.com%2Fp%2Fi-tested-14-analytics-agents-so-you%3Futm_source=tldrdata/1/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/9yEIqOxu2V3FKPD4tUYE2m4gOBA_lW_8gOfhM2pP6Ic=440">
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<strong>I tested 14 analytics agents - so you don't have to (15 minute read)</strong>
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A 2026 benchmark of 14 agentic analytics tools tested reliability, UX, speed, and cost on real production data. The conclusion is that AI-native BI tools currently offer the best overall experience, but at the expense of higher cost and migration effort.
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<h1><strong>Quick Links</strong></h1>
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<strong>Introducing Meta Segment Anything Model 3 and Segment Anything Playground (9 minute read)</strong>
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Meta's Segment Anything Model 3 (SAM 3) introduces promptable concept segmentation via text and exemplar prompts, enabling state-of-the-art detection, segmentation, and tracking of any visual concept across images and video.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flevelup.gitconnected.com%2Fbuilding-a-containerized-ml-inference-service-with-automated-ci-cd-1e987e8d5dfa%3Futm_source=tldrdata/1/0100019bc156f6ac-764a8588-a8f0-42dd-9441-9c8c96d52e81-000000/G1m8VsHFxqRrTYL9CN12m0i0kzEoriwdIKzVzoq8hlk=440">
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<strong>Building a Containerized ML Inference Service with Automated CI/CD (2 minute read)</strong>
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Step-by-step template to deploy a Dockerized DistilGPT2 with security scans and GitHub Actions in under 20 minutes.
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